Remote sensing image feature discretization method based on rough-fuzzy model

ABSTRACT

Provided is a remote sensing image feature discretization method based on rough-fuzzy model, comprising the following steps: listing the digital number in each band and category of a selected sample in remote sensing images, and building an image information decision table based on the digital number and category; initializing the class center of each category and the membership degree of a sample example relative to the class center; updating the class center of each category and the membership degree of the sample example relative to the class center iteratively, and obtaining the final value of the class center of each category and the final value of the membership degree; building a rough-fuzzy set, computing the mean approximation accuracy of the rough-fuzzy set, discretizing the image information decision table, evaluating the discretization results based on the mean approximation accuracy and a genetic algorithm, and selecting an optimal discretization solution.

TECHNICAL FIELD OF THE INVENTION

The present invention relates to the field of remote sensing image feature extraction technology, in particular to a remote sensing image feature discretization method based on a rough-fuzzy model.

BACKGROUND OF THE INVENTION

Feature discretization is an important massive data preprocessing technology in industrial control. It improves an efficiency of edge cloud computing by transforming continuous features into discrete features, thus meeting the requirement for high-quality cloud service. Compared with other discretization methods, the discretization method based on a rough set has achieved good results in many applications because it can make full use of the known knowledge bases without any priori information. However, the equivalence class of the rough set is a common set, which has difficulty in describing fuzzy components inside the data, and the obtained accuracy is low on some problems of complex data types in the big data environment.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide the remote sensing image feature discretization method based on the rough-fuzzy model to solve the problems raised in the background.

The present invention is realized by the following technical solution: the remote sensing image feature discretization method based on the rough-fuzzy model, comprising the following steps:

listing the digital number in each band and category of a selected sample in the remote sensing images, and building an image information decision table based on the digital number and category;

initializing a class center of each category and a membership degree of a sample example relative to the class center;

updating the class center of each category and the membership degree of the sample example relative to the class center iteratively, and obtaining a final value of the class center of each category and a final value of the membership degree;

building a rough-fuzzy set, computing a mean approximation accuracy of the rough-fuzzy set, discretizing the image information decision table, evaluating discretization results based on the mean approximation accuracy and a genetic algorithm, and selecting an optimal discretization solution.

Preferably, building the image information decision table based on the digital number and the category, comprising:

selecting a plurality of samples from the remote sensing images;

obtaining the digital number in each band of each selected sample by an image sampling, and labeling a corresponding land cover type in each sample, wherein all the samples constitute a sample set;

building a decision table matrix with the band as a condition attribute and with the surface feature type as a decision attribute according to the sample set with the digital numbers and land type labels.

Preferably, initializing the class center of each category and the membership degree of the sample example relative to the class center, comprising:

initializing the class center c_(j) ⁰ of each category as:

${PM^{0}} = \begin{bmatrix} {f_{1}\left( x_{1} \right)} & {f_{1}\left( x_{2} \right)} & \ldots & {f_{1}\left( x_{N} \right)} \\ {{f_{2}\left( x_{1} \right)}\begin{matrix}  \cdot \\  \cdot \\  \cdot  \end{matrix}} & {{f_{2}\left( x_{2} \right)}\begin{matrix}  \cdot \\  \cdot \\  \cdot  \end{matrix}} & {\ldots\begin{matrix} \cdots \\ \cdots \\ \cdots \end{matrix}} & {{f_{2}\left( x_{N} \right)}\begin{matrix}  \cdot \\  \cdot \\  \cdot  \end{matrix}} \\ {f_{C}\left( x_{1} \right)} & {f_{C}\left( x_{2} \right)} & \ldots & {f_{C}\left( x_{N} \right)} \end{bmatrix}$ ${f_{j}\left( x_{i} \right)} = \left\{ \begin{matrix} {1,} & {x_{i}{belongs}{to}{class}j} \\ {0,} & {otherwise} \end{matrix} \right.$ where1 ≤ i ≤ Nand1 ≤ j ≤ C

wherein N is the number of samples, and C is the number of categories;

initializing the membership degree of the sample example relative to the class center as:

$u_{ij}^{0} = {1/{\sum_{k = 1}^{C}\left( \frac{\sum_{h = 1}^{M}\left( {x_{ih} - c_{jh}} \right)^{2}}{\sum_{h = 1}^{M}\left( {x_{ih} - c_{kh}} \right)^{2}} \right)}}$

wherein M is the number of attributes, x_(ih) is a value of the sample x_(i) on the h th attribute, and c_(jh) is a value of the current class center c_(j) on the h th attribute.

Preferably, updating the class center of each category and the membership degree of the sample example relative to the class center iteratively, comprising:

-   -   updating the class center c° iteratively by the following         formula:

c _(j) ¹=Σ_(i=1) ^(N)((u _(ij) ⁰)² ×x _(i))/Σ_(i=1) ^(N)(u _(ij) ⁰)²

wherein c_(j) ¹ is an updated class center;

-   -   computing the new membership degree u_(ij) ¹ iteratively         according to the updated class center c_(j) ¹.

Preferably, stopping iterative computations of u_(ij) and c_(j) and obtaining the final value of the class center of each category and the final value of the membership degree when the following conditions are met:

max_(ij) {|u _(ij) ^(t+1) −u _(ij) ^(t)|}<ε

wherein t is the number of iterations, and ε is an error threshold.

Preferably, building the rough-fizzy set, and computing the mean approximation accuracy of the rough-fuzzy set, comprising:

-   -   creating a rough-fuzzy set based on each category:

A _(j)(x _(i))=u _(ij), 1≤i≤N, 1≤j≤C;

computing a lower approximation of the rough-fuzzy set:

${R_{-}{A_{j}\left( x_{i} \right)}} = {\inf\limits_{y \in U}\left\{ {A_{j}(y)} \middle| {\left( {x_{i},y} \right) \in R} \right\}}$

computing an upper approximation of the rough-fuzzy set:

${R^{-}{A_{j}\left( x_{i} \right)}} = {\sup\limits_{y \in U}\left\{ {A_{j}(y)} \middle| {\left( {x_{i},y} \right) \in R} \right\}}$

computing an mean approximation accuracy of the rough-fuzzy set:

$\overset{\_}{\eta} = {\frac{1}{C}{\sum_{j = 1}^{C}{\frac{❘{R_{-}A_{j}}❘}{❘{R^{-}A_{j}}❘}.}}}$

Preferably, discretizing the image information decision table, comprising:

sorting and deduplicating in the band, according to brightness values, all digital numbers contained in each band in the image information decision table, and obtaining initial breakpoints of all bands, wherein the initial breakpoints constitute a candidate breakpoint set of the remote sensing images, and each subset of the candidate breakpoint set corresponds to a discretization solution of the remote sensing images.

Preferably, evaluating the discretization results based on the mean approximation accuracy and the genetic algorithm, and selecting the optimal discretization solution, comprising:

building a fitness function based on the mean approximation accuracy and the number of breakpoints:

Fit=α×|D|+β×η

-   -   where α≥0, β≥0, and α+β=1

wherein |D| is the number of breakpoints of the discretization solution D, and both α and β are weight coefficients;

taking a discretization solution as an individual of a group in the genetic algorithm, computing the fitness values of all the individuals in the group iteratively based on the fitness function, and finding an individual with the greatest fitness value, wherein the discretization solution corresponding to the individual with the greatest fitness value is the optimal discretization solution.

Compared with the prior art, the present invention has the beneficial effects as follows:

According to the remote sensing image feature discretization method based on the rough-fuzzy model provided by the present invention, continuous features in spectral information can be transformed into discrete features closer to the representation of a knowledge layer after the remote sensing images are processed by feature discretization based on the rough-fuzzy model, thus greatly cutting down a data size, reducing system overhead, lightening system loads, removing redundant information, reducing a data inconsistency, enhancing a robustness and a generalization capability of learning algorithms, and improving a classification accuracy of the remote sensing images.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to give a clearer description of the technical solutions in the embodiments of the present invention, the drawings to be used in the description of the embodiment will be briefly introduced below. Obviously, the drawings in the following description are only preferred embodiments of the present invention, and other drawings can also be obtained according to these drawings without contributing creative effort for those of ordinary skill in the art.

FIG. 1 is a flow chart of the remote sensing image feature discretization method based on the rough-fuzzy model provided in the present invention;

FIG. 2 is a classification effect map of the Landsat 8 image provided in the embodiment of the present invention; and

FIG. 3 is a classification effect map of the GF-2 image provided in the embodiment of the present invention.

DETAILED DESCRIPTION OF EMBODIMENTS

The technical solutions in the embodiments of the present invention will be clearly and completely described below in combination with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the present invention requested, but only represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without contributing their creative effort shall be included in the protection scope of the present invention.

Refer to FIG. 1 , the remote sensing image feature discretization method based on the rough-fuzzy model, comprising the following steps:

Step 101: listing the digital number in each band and category of the selected sample in the remote sensing images, and building the image information decision table based on the digital number and category;

listing the digital number in each band and category of the selected sample in the remote sensing images through processes of a radiometric calibration, an atmospheric correction, and a sampling of the remote sensing images, and building the image information decision table based on the digital number and category.

Specifically, this step can be implemented in the following ways:

selecting several regions with significant features from the remote sensing images discrete training data sets for discretization, separating band attributes from the training data sets, and labeling each region respectively. Here, the methods of extracting information from the remote sensing images and separating various information such as geographical coordinate information and band attributes can be realized by the prior art, which are not improved in this discretization method, so it is simplified and not described in detail here.

selecting a plurality of the samples by image sampling; obtaining the digital number of each selected sample and labeling the corresponding land cover type in each sample, wherein all the samples constitute the sample set. For example, assigning values to the samples in the regions in a category attribute column according to a region labeling; next, picking out the band attributes and category attributes of the samples from a variety of information obtained after separation from the training data sets (discarding other information), merging the band attributes and category attributes in order to build the decision table matrix; finally, sorting the samples according to the values in the category attribute column, and generating the sample set with digital numbers and land type labels.

building the decision table matrix with the band as the condition attribute and the surface feature type as the decision attribute according to the sample set with digital numbers and land type labels, and the matrix style is shown below: S=(U, R, V, ƒ).

wherein U is anon-null limited universe of discourse, R is an attribute set, V is a value domain, ƒ is a mapping function of an object to each attribute value domain.

Step 102: initializing the class center of each category and the membership degree of the sample example relative to the class center;

assuming that the number of samples contained in U is N, the number of categories is C, the number of attributes is M, x_(ih) is the value of sample x_(i) on the h th attribute, 1≤i≤N, 1≤h≤M;

based on the samples contained in U, building an initial fuzzy segmentation matrix as:

${PM^{0}} = \begin{bmatrix} {f_{1}\left( x_{1} \right)} & {f_{1}\left( x_{2} \right)} & \ldots & {f_{1}\left( x_{N} \right)} \\ {{f_{2}\left( x_{1} \right)}\begin{matrix}  \cdot \\  \cdot \\  \cdot  \end{matrix}} & {{f_{2}\left( x_{2} \right)}\begin{matrix}  \cdot \\  \cdot \\  \cdot  \end{matrix}} & {\ldots\begin{matrix} \cdots \\ \cdots \\ \cdots \end{matrix}} & {{f_{2}\left( x_{N} \right)}\begin{matrix}  \cdot \\  \cdot \\  \cdot  \end{matrix}} \\ {f_{C}\left( x_{1} \right)} & {f_{C}\left( x_{2} \right)} & \ldots & {f_{C}\left( x_{N} \right)} \end{bmatrix}$ ${wherein},{{f_{j}\left( x_{i} \right)} = \left\{ {{\begin{matrix} {1,} \\ {0,} \end{matrix}\begin{matrix} {x_{i}{belongs}{to}{class}j} \\ {otherwise} \end{matrix}{where}1} \leq i \leq {N{and}1} \leq j \leq C} \right.}$

regarding the initial fuzzy partition matrix as an initialized class center of each category;

based on the initialized class center, initializing the membership degree of the sample example relative to the class center as:

$u_{ij}^{0} = {1/{\sum_{k = 1}^{C}\left( \frac{\sum_{h = 1}^{M}\left( {x_{ih} - c_{jh}} \right)^{2}}{\sum_{h = 1}^{M}{= \left( {x_{ih} - c_{kh}} \right)^{2}}} \right)}}$

wherein M is the number of attributes, x_(ih) is the value of the sample x_(i) on the h th attribute, and c_(jh) is the value of the current class center c_(j) on the h the attribute.

Step 103: updating the class center of each category and the membership degree of the sample example relative to the class center iteratively, and obtaining the final value of the class center of each category and the final value of the membership degree, specifically comprising:

updating the class center c_(j) ⁰ iteratively by the following formula:

c _(j) ¹=Σ_(i=1) ^(N)((u _(ij) ⁰)² ×x _(i))/Σ_(i=1) ^(N)(u _(ij) ⁰)²

wherein c_(j) ¹ is the updated class center;

computing the new membership degree u_(ij) ¹ iteratively according to the updated class center c_(j) ¹.

stopping the iterative computations of u_(ij) and c_(j) and obtaining the final value of the class center of each category and the final value of the membership degree when the following conditions are met:

max_(ij) {|u _(ij) ^(t+1) −u _(ij) ^(t)|}<ε

wherein t is the number of iterations, ε is the error threshold.

Step 104: building the rough-fuzzy set, computing the mean approximation accuracy of the rough-fuzzy set, discretizing the image information decision table, evaluating the discretization results based on the mean approximation accuracy and the genetic algorithm, and selecting the optimal discretization solution.

The detailed process comprises:

creating a rough-fuzzy set for each category based on the obtained final value of the membership degree:

A _(j)(x _(i))=u _(ij), 1≤i≤N, 1≤j≤C;

computing the lower approximation of the rough-fuzzy set:

${R_{-}{A_{j}\left( x_{i} \right)}} = {\inf\limits_{y \in U}\left\{ {A_{j}(y)} \middle| {\left( {x_{i},y} \right) \in R} \right\}}$

computing the upper approximation of the rough-fuzzy set:

${R^{-}{A_{j}\left( x_{i} \right)}} = {\sup\limits_{y \in U}\left\{ {A_{j}(y)} \middle| {\left( {x_{i},y} \right) \in R} \right\}}$

computing the mean approximation accuracy of the rough-fuzzy set:

$\overset{\_}{\eta} = {\frac{1}{C}{\sum_{j = 1}^{C}{\frac{❘{R_{-}A_{j}}❘}{❘{R^{-}A_{j}}❘}.}}}$

Discretization means dividing the continuous features (also known as continuous attributes) into a finite number of subintervals by a specific method and associating these subintervals with a set of discrete values (also known as the breakpoints); the discretization can greatly cut down the data size, thus improving a massive data processing efficiency of the edge cloud computing at an edge end, and greatly relieving a pressure of transmitting data back to a central cloud.

Therefore, in this embodiment, the image information decision table is discretized, comprising:

sorting and deduplicating in the band, according to brightness values, all digital numbers contained in each band in the image information decision table and obtaining the initial number of the breakpoints of several bands, wherein the initial number of the breakpoints is the initial discretization solution.

evaluating the discretization results based on the mean approximation accuracy and the genetic algorithm, and selecting the optimal discretization solution, comprising:

building the fitness function based on the mean approximation accuracy and the number of the breakpoints:

Fit=α×|D|+β×η

-   -   where α≥0, β≥0, and α+β=1

wherein |D| is the number of the breakpoints of the discretization solution D;

wherein α and β are the weight coefficients. The selection of weight coefficients is an open question, for no given weight coefficient can adapt to all data sets, and a rationality of weight setting is generally judged by characteristics of data sets and experimental observations; the classification accuracy is directly related to the mean approximation accuracy of the rough-fuzzy sets in this embodiment, and therefore, in this embodiment, α=0.1, and β6=0.9.

The genetic algorithm is a globally optimized probabilistic evolutionary algorithm, which has achieved good performance on many optimization problems. The genetic algorithm evaluates the quality of individuals in the group through the fitness function and transforms a problem-solving process into a process similar to the crossover and mutation of chromogenes in biological evolution. Usually, the genetic algorithm can obtain better optimization results quickly than some conventional optimization algorithms when complex combinational optimization problems are being solved. However, the genetic algorithm cannot directly deal with parameters of a problem space, thus the problem to be solved must be expressed as a chromosome or individual of a genetic space by encoding. This transformation operation is called genetic encoding. The genetic encoding adopts the following specifications: (1) completeness: all candidate solutions in the problem space can be represented as the chromosomes in the genetic space; (2) soundness: the chromosomes in the genetic space can correspond to all the candidate solutions in the problem space; (3) non-redundancy: there is a one-to-one correspondence between the chromosomes and the candidate solutions.

The discretization problem can be regarded as the selection of candidate breakpoints. Each chromosome in the group represents a possible discretization solution. The chromosome length is equal to the number of candidate breakpoints. We encode the candidate breakpoints in the way of binary encoding. Each bit in a binary code corresponds to a candidate breakpoint, and the values ‘1’ and ‘0’ represent breakpoint selected and breakpoint unselected respectively. A set of selected candidate breakpoints is a possible discretization solution.

In this embodiment, a plurality of discretization solutions is regarded as group individuals in the genetic algorithm; the individuals with the maximum fitness value are iteratively computed and looked for through the evolution function of the genetic algorithm, and the discretization solution corresponding to the individual with the maximum fitness value is the optimal discretization solution.

Meanwhile, when the number of iterations satisfies the requirement, the discretization solution corresponding to the number of iterations is the optimal discretization solution.

When the method of the present invention is verified through experiments, the test data sets used in the experiments are Landsat 8 satellite images from the southeast region in China and the GF-2 images from South China Sea Islands.

The Landsat 8 satellite data contain 7 bands, and GF-2 satellite data contain 4 bands; the surface features on the Landsat 8 image in the experiments are divided into seven categories, i.e., broadleaf, town, conifer, farmland, Phyllostachys praecox, water, and moso bamboo; the surface features on the GF-2 image are divided into five categories, i.e., construction land, bareland, farmland, vegetation, and water.

Several regions covering the seven categories are randomly selected from the Landsat 8 image and labeled, integrated and used as training samples to be discretized, and there are a total of 2621 examples. Among them, there are 308 broadleaf examples, 245 town examples, 322 conifer examples, 675 farmland examples, 296 Phyllostachys praecox examples, 262 water examples, and 513 moso bamboo examples.

Another group of samples of the same number as the training samples is used as a test set.

Among them, there are 308 broadleaf examples, 245 town examples, 322 conifer examples, 675 farmland examples, 296 Phyllostachys praecox examples, 262 water examples, and 513 moso bamboo examples.

All the digital numbers contained in each band are sorted and deduplicated in the band according to the brightness values, so that the initial numbers of the breakpoints of seven bands are obtained, which are 1314, 1517, 1056, 1211, 1086, 1920 and 1832, with 9936 breakpoints in total.

Similarly, in the GF-2 image, there are a total of 7554 examples for the training samples to be discretized. Among them, there are 2094 construction land examples, 775 bareland examples, 1478 farmland examples, 2251 vegetation examples, and 956 water examples. We take another group of samples of the same number as the training samples as the test set. Among them, there are 2094 construction land examples, 775 bareland examples, 1478 farmland examples, 2251 vegetation examples, and 956 water examples.

All the digital numbers contained in each band are sorted and deduplicated in the band according to the brightness values respectively, so that the initial numbers of breakpoints of four bands are obtained, which are 3685, 3769, 2535 and 757 respectively, with 10746 breakpoints in total. In a methylation data set, there are a total of 3709 examples for the training samples to be discretized. Among them, there are 1290 examples of 6 mA methylation, and 2419 examples of 4mC methylation. There are a total of 1500 examples for the test samples. Among them, there are 500 examples of 6 mA methylation and 1000 examples of 4mC methylation.

All values contained in each attribute of the first group of methylation training set are sorted and deduplicated in the attribute respectively, so that the initial numbers of the breakpoints of three attributes are obtained, which are 1718, 1748 and 960 respectively, with 4426 breakpoints in total. All values contained in each attribute of the second group of methylation training set are sorted and deduplicated in the attribute respectively, so that the initial numbers of the breakpoints of three attributes are obtained, which are 564, 1748 and 960 respectively, with 3272 breakpoints in total. In a banknote verification data set, there are 1072 examples for the training samples to be discretized. Among them, there are 562 examples of genuine banknote samples and 510 examples of counterfeit banknote samples. There are 300 examples of test samples in total. Among them, there are 200 examples of genuine banknote samples and 100 examples of counterfeit banknote samples. All values contained in each attribute are sorted and deduplicated in the attribute respectively, so that the initial numbers of the breakpoints of four attributes are obtained, which are 1052, 996, 1015 and 940 respectively, with 4003 breakpoints in total.

In order to verify an effectiveness of the proposed algorithm, an RFMD method disclosed in the present invention is compared with the discretization results obtained by an RS-GA method, an EDiRa method, a CVD method and an RLGA method for evaluation mainly in terms of a data consistency and number of intervals.

The numbers of intervals in all bands and the results of data inconsistency obtained by the methods of RFMD, RS-GA, EDiRa, CVD and RLGA on the Landsat 8 image are shown in Table 1 and Table 2.

TABLE 1 Method B1 B2 B3 B4 B5 B6 B7 RFMD 109 67 58 64 63 55 71 RS-GA 153 69 56 52 76 103 61 EDiRa 135 71 86 45 52 58 73 CVD 98 73 65 67 72 58 71 RLGA 120 67 65 52 63 55 71

TABLE 2 Method Inconsistencies Discrete intervals RFMD 0 487 RS-GA 5 570 EDiRa 13 520 CVD 17 504 RLGA 2 493

It can be seen that the number of intervals obtained by the RFMD algorithm is 487, which is the least among all algorithms, and there is no data error. The number of intervals of the RS-GA algorithm is the largest among all algorithms, which reaches 570, followed by the EDiRa algorithm with the number of intervals of 520, and the numbers of data errors obtained by the two algorithms are 5 and 13 respectively. The number of intervals of the CVD algorithm is only 17 more than that of the RFMD algorithm, but the number of data errors is the largest among all algorithms, which is 17. The number of intervals of the RLGA algorithm is 493 with 2 data errors, and the performance of the RLGA algorithm is second only to the RFMD algorithm.

The numbers of intervals in all bands and the results of data inconsistency obtained by the methods of RFMD, RS-GA, EDiRa, CVD and RLGA on the GF-2 image are shown in Table 3 and Table 4.

TABLE 3 Method B1 B2 B3 B4 RFMD 267 458 207 103 RS-GA 389 502 397 103 EDiRa 405 517 253 132 CVD 299 461 278 115 RLGA 267 461 247 103

TABLE 4 Method Inconsistencies Discrete intervals RFMD 0 1035 RS-GA 14 1391 EDiRa 25 1307 CVD 30 1153 RLGA 7 1078

It can be seen that the number of intervals obtained by the RFMD algorithm is 1035, which is the least among all the algorithms, and there is no data error. The number of intervals of the RS-GA algorithm is the largest among all the algorithms, which reaches 1391, followed by the EDiRa algorithm with the number of intervals of 1307, and the numbers of data errors obtained by the two algorithms are 14 and 25 respectively. The number of intervals of the CVD algorithm is 118 more than that of the RFMD algorithm, and the number of data errors is the largest among all the algorithms, which is 30. The number of intervals of the RLGA algorithm is 1078 with 7 data errors, and the performance of the RLGA algorithm is second only to the RFMD algorithm.

The numbers of intervals for all attributes and the results of data inconsistency obtained by the methods of RFMD, RS-GA, EDiRa, CVD and RLGA on the first group of methylation data set are shown in Table 5 and Table 6.

TABLE 5 Method Mean Model prediction Interpulse duration ratio RFMD 210 189 138 RS-GA 244 269 156 EDiRa 241 296 34 CVD 205 229 129 RLGA 225 260 71

TABLE 6 Method Inconsistencies Discrete intervals RFMD 12 537 RS-GA 80 669 EDiRa 113 571 CVD 259 563 RLGA 71 556

It can be seen that the number of intervals obtained by the RFMD algorithm is 537, which is the least among all algorithms, and the number of data errors is also the least among all algorithms, which is 12. The number of intervals of the RS-GA algorithm is the largest among all algorithms, which reaches 669, followed by the EDiRa algorithm with the number of intervals of 571, and the numbers of data errors obtained by the two algorithms are 80 and 113 respectively. The number of intervals of the CVD algorithm is 26 more than that of the RFMD algorithm, and the number of data errors is the largest among all algorithms, which is 259. The number of intervals of the RLGA algorithm is 556 with 71 data errors, and the performance of the RLGA algorithm is second only to the RFMD algorithm.

The numbers of intervals for all attributes and the results of data inconsistency obtained by the methods of RFMD, RS-GA, EDiRa, CVD and RLGA on the second group of methylation data set are shown in Table 7 and Table 8.

TABLE 7 Method Error Model prediction Interpulse duration ratio RFMD 141 332 242 RS-GA 180 448 243 EDiRa 148 415 219 CVD 150 373 228 RLGA 143 363 216

TABLE 8 Method Inconsistencies Discrete intervals RFMD 0 715 RS-GA 6 871 EDiRa 11 782 CVD 15 751 RLGA 3 722

It can be seen that the number of intervals obtained by the RFMD algorithm is 715, which is the least among all algorithms, and there is no data error. The number of intervals of the RS-GA algorithm is the largest among all algorithms, which reaches 871, followed by the EDiRa algorithm with the number of intervals of 782, and the numbers of data errors obtained by the two algorithms are 6 and 11 respectively. The number of intervals of the CVD algorithm is 36 more than that of the RFMD algorithm, and the number of data errors is the largest among all algorithms, which is 15. The number of intervals of the RLGA algorithm is 722 with 3 data errors, and the performance of the RLGA algorithm is second only to the RFMD algorithm.

The numbers of intervals for all attributes and the results of data inconsistency obtained by the methods of RFMD, RS-GA, EDiRa, CVD and RLGA on the banknote verification data set are shown in Table 9 and Table 10.

TABLE 9 Method Variance Skewness Kurtosis Entropy RFMD 6 7 7 7 RS-GA 11 11 13 4 EDiRa 14 8 12 3 CVD 10 10 12 3 RLGA 6 8 8 8

TABLE 10 Method Inconsistencies Discrete intervals RFMD 0 27 RS-GA 1 39 EDiRa 2 37 CVD 3 35 RLGA 0 30

It can be seen that the number of intervals obtained by the RFMD algorithm is 27, which is the least among all algorithms, and there is no data error. The number of intervals of the RS-GA algorithm is the largest among all algorithms, which reaches 39, followed by the EDiRa algorithm with the number of intervals of 37, and the numbers of data errors obtained by the two algorithms are 1 and 2 respectively. The number of intervals of the CVD algorithm is 8 more than that of the RFMD algorithm, and the number of data errors is the largest among all algorithms, which is 3. The number of intervals of the RLGA algorithm is 30 with no data error, and the performance of the RLGA algorithm is second only to the RFMD algorithm.

Although discretization criteria used by the EDiRa and the CVD have certain rationality, the relatively fixed division criteria cannot comprehensively measure the discrete intervals. In addition, both the EDiRa and the CVD need the distribution information of sample attribute values in the data set to improve an accuracy of interval division. Since the discretization criteria based on the rough set are used, good results can also be achieved by RS-GA without any prior information.

The performance of RS-GA is often poor in complex types of data sets due to a lack of the ability to describe fuzzy components in data. RLGA introduces reinforcement learning mechanisms into crossover and mutation operations respectively to improve a search efficiency of the genetic algorithm and keeps looking for a solution with the least number of intervals while controlling the data errors at a low level. Like RS-GA, the fitness function used by RLGA is only based on the rough set, and RLGA lacks the ability to describe fuzzy components in data.

RFMD combines the advantages of the rough set and the fuzzy set, fully takes account of a correlation between fuzziness and attributes within the data and determines the breakpoints in a plurality of continuous variables by evolutionary search. In this way, the performance of RFMD is greatly improved, so that RFMD can adapt to a majority of complex data sets. Therefore, the discretization result obtained by RFMD is the best of the five algorithms. The key differences between them are shown in Table 11.

TABLE 11 Prior- Method Direction Attributes knowledge Uncertainty RFMD Evolutionary Multivariate No need Incompleteness search & Fuzziness RS-GA Evolutionary Multivariate No need Incompleteness search EDiRa Top-Down Univariate Need Incompleteness CVD Bottom-Up Univariate Need Incompleteness RLGA Evolutionary Multivariate No need Incompleteness search

Neural network classifiers are trained respectively for discretized samples of these five algorithms, so that the classification results of the Landsat 8 image and the GF-2 image are obtained, as shown in Table 12 and Table 13

TABLE 12 Method Overall accuracy Kappa coefficient RFMD 0.9428 0.9314 RS-GA 0.9275 0.9131 EDiRa 0.9222 0.9067 CVD 0.8993 0.8793 RLGA 0.9351 0.9223

TABLE 13 Method Overall accuracy Kappa coefficient RFMD 0.9734 0.9655 RS-GA 0.9297 0.9083 EDiRa 0.9076 0.8795 CVD 0.8752 0.8385 RLGA 0.9314 0.9106

It can be seen that the classification accuracy of the method disclosed in the present invention has the best performance among the five algorithms. The number of data errors of RS-GA, EDiRa and RLGA is less than that of CVD, and accordingly, RS-GA, EDiRa and RLGA have a higher classification accuracy than that of CVD.

FIG. 2 is a classification effect map of the Landsat 8 image obtained by the method disclosed in the present invention. It can be seen from the figure that the texture of the surface feature information in the figure is clear, the boundaries of different types of surface features are obvious, and there are almost no noise spots. The seven categories of regions, i.e., broadleaf, town, conifer, farmland, Phyllostachys praecox, water, and moso bamboo, on the image can be effectively identified.

FIG. 3 is a classification effect map of the GF-2 image obtained by the method disclosed in the present invention. The texture of the surface feature information in the figure is clear, and the boundaries of different types of the surface features are very obvious. The five categories of regions, i.e., construction land, bareland, farmland, vegetation, and water, on the image can be effectively identified.

TABLE 14 Method Overall accuracy Kappa coefficient RFMD 0.9687 0.9308 RS-GA 0.9380 0.8626 EDiRa 0.9233 0.8310 CVD 0.9093 0.8031 RLGA 0.9453 0.8791

TABLE 15 Method Overall accuracy Kappa coefficient RFMD 0.9633 0.9190 RS-GA 0.9247 0.8331 EDiRa 0.9100 0.8035 CVD 0.8960 0.7752 RLGA 0.9387 0.8643

TABLE 16 Method Overall accuracy Kappa coefficient RFMD 0.9933 0.9851 RS-GA 0.9500 0.8872 EDiRa 0.9100 0.8010 CVD 0.8833 0.7494 RLGA 0.9767 0.9479

Tables 14, 15 and 16 are classification results of the five algorithms on the first group of methylation data set, the second set of methylation data set, and the banknote verification data set respectively. It can be seen that the classification accuracy of RFMD is the highest among all algorithms. Therefore, the discretization solution obtained by RFMD can achieve good results in terms of the classification accuracy.

The above statements are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present invention shall be included in the protection scope of the present invention. 

1. A remote sensing image feature discretization method based on a rough-fuzzy model, comprising the steps of: listing a digital number in each band and category of a selected sample in remote sensing images, and building an image information decision table based on the digital number and category; initializing a class center of each category and a membership degree of a sample example relative to the class center; updating the class center of each category and the membership degree of the sample example relative to the class center iteratively, and obtaining a final value of the class center of each category and a final value of the membership degree; and building a rough-fuzzy set, computing a mean approximation accuracy of the rough-fuzzy set, discretizing the image information decision table, evaluating discretization results based on the mean approximation accuracy and a genetic algorithm, and selecting an optimal discretization solution.
 2. The method according to claim 1, whereby building the image information decision table based on the digital number and category, comprises the steps of: selecting a plurality of samples from the remote sensing images; obtaining the digital number in each band of each selected sample by image sampling, and labeling a corresponding land cover type in each sample, wherein all the samples constitute a sample set; and building a decision table matrix with the band as a condition attribute and with the surface feature type as a decision attribute according to the sample set with digital number and land type label.
 3. The method according to claim 2, whereby initializing the class center of each category and the membership degree of the sample example relative to the class center, comprises the steps of: initializing the class center c_(j) ⁰ of each category as: ${PM^{0}} = \begin{bmatrix} {f_{1}\left( x_{1} \right)} & {f_{1}\left( x_{2} \right)} & \ldots & {f_{1}\left( x_{N} \right)} \\ {{f_{2}\left( x_{1} \right)}\begin{matrix}  \cdot \\  \cdot \\  \cdot  \end{matrix}} & {{f_{2}\left( x_{2} \right)}\begin{matrix}  \cdot \\  \cdot \\  \cdot  \end{matrix}} & {\ldots\begin{matrix} \cdots \\ \cdots \\ \cdots \end{matrix}} & {{f_{2}\left( x_{N} \right)}\begin{matrix}  \cdot \\  \cdot \\  \cdot  \end{matrix}} \\ {f_{C}\left( x_{1} \right)} & {f_{C}\left( x_{2} \right)} & \ldots & {f_{C}\left( x_{N} \right)} \end{bmatrix}$ ${f_{j}\left( x_{i} \right)} = \left\{ {{\begin{matrix} {1,} & {x_{i}{belongs}{to}{class}j} \\ {0,} & {otherwise} \end{matrix}{where}1} \leq i \leq {N{and}1} \leq j \leq C} \right.$ wherein N is the number of samples, and C is the number of categories; and initializing the membership degree of the sample example relative to the class center as: $u_{ij}^{0} = {1/{\sum_{k = 1}^{C}\left( \frac{\sum_{h = 1}^{M}\left( {x_{ih} - c_{jh}} \right)^{2}}{\sum_{h = 1}^{M}{= \left( {x_{ih} - c_{kh}} \right)^{2}}} \right)}}$ wherein M is the number of attributes, x_(ih) is a value of the sample x_(i) on the h th attribute, and c_(jh) is a value of the current class center c_(j) on the h th attribute.
 4. The method according to claim 3, whereby updating the class center of each category and the membership degree of the sample example relative to the class center iteratively, comprises the steps of: updating the class center c_(j) ⁰ iteratively by the following formula: c _(j) ¹=Σ_(i=1) ^(N)((u _(ij) ⁰)² ×x _(i))/Σ_(i=1) ^(N)(u _(ij) ⁰)² wherein c_(j) ¹ is an updated class center; computing the new membership degree u_(ij) ¹ iteratively according to the updated class center c_(j) ¹.
 5. The method according to claim 4, whereby stopping iterative computations of u_(ij) and c_(j), and obtaining the final value of the class center of each category and the final value of the membership degree when the following conditions are met: max_(ij) {|u _(ij) ^(t+1) −u _(ij) ^(t)}<ε wherein t is the number of iterations, ε is an error threshold.
 6. The method according to claim 5, whereby building the rough-fuzzy set, and computing the mean approximation accuracy of the rough-fuzzy set, comprises the steps of: creating the rough-fuzzy set based on each category: A _(j)(x _(i))=u _(ij), 1≤i≤N, 1≤j≤C; computing a lower approximation of the rough-fuzzy set: ${R_{-}{A_{j}\left( x_{i} \right)}} = {\inf\limits_{y \in U}\left\{ {A_{j}(y)} \middle| {\left( {x_{i},y} \right) \in R} \right\};}$ computing ab upper approximation of the rough-fuzzy set: ${R^{-}{A_{j}\left( x_{i} \right)}} = {\sup\limits_{y \in U}\left\{ {A_{j}(y)} \middle| {\left( {x_{i},y} \right) \in R} \right\};}$  and computing the mean approximation accuracy of the rough-fuzzy set: $\overset{\_}{\eta} = {\frac{1}{C}{\sum_{j = 1}^{C}{\frac{❘{R_{-}A_{j}}❘}{❘{R^{-}A_{j}}❘} \circ}}}$
 7. The method according to claim 6, whereby discretizing the image information decision table, comprises the steps of: sorting and deduplicating in the band, according to brightness values, all digital numbers contained in each band in the image information decision table, and obtaining initial breakpoints of all bands, wherein the initial breakpoints constitute a candidate breakpoint set of the remote sensing images, and each subset of the candidate breakpoint set corresponds to a discretization solution of the remote sensing images.
 8. The method according to claim 7, whereby evaluating the discretization results based on the mean approximation accuracy and the genetic algorithm, and selecting the optimal discretization solution, comprises the steps of: building a fitness function based on the mean approximation accuracy and the number of breakpoints: Fit=α×|D|+β×η where α≥0, β≥0, and α+β=1 wherein |D| is the number of breakpoints of the discretization solution D, and both α and β are weight coefficients; taking a discretization solution as an individual of a group in the genetic algorithm, computing the fitness values of all the individuals in the group iteratively based on the fitness function, and finding an individual with the greatest fitness value, wherein the discretization solution corresponding to the individual with the greatest fitness value is the optimal discretization solution. 